Abstract
We compare two recently proposed approaches for representing probability distributions over the space of permutations in the context of multi-target tracking. We show that these two representations, the Fourier approximation and the information form approximation can both be viewed as low dimensional projections of a true distribution, but with respect to different metrics. We identify the strengths and weaknesses of each approximation, and propose an algorithm for converting between the two forms, allowing for a hybrid approach that draws on the strengths of both representations. We show experimental evidence that there are situations where hybrid algorithms are favorable.
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Jiang, X., Huang, J., Guibas, L. (2011). Fourier-Information Duality in the Identity Management Problem. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science(), vol 6912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23783-6_7
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DOI: https://doi.org/10.1007/978-3-642-23783-6_7
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